class_docstr: This :class:`Metric` takes the best span string computed by a model, along with the answer
  strings labeled in the data, and computed exact match and F1 score using the official SQuAD
  evaluation script.
docstr: Parameters
  ----------
  value : ``float``
      The value to average.
docstr_tok: ["Parameters","--","--","--","--","--","value",":","``","float","``","The","value","to","average","."]
code: def __call__(self, best_span_string, answer_strings):
      """
      Parameters
      ----------
      value : ``float``
          The value to average.
      """
      exact_match = squad_eval.metric_max_over_ground_truths(
          squad_eval.exact_match_score, best_span_string, answer_strings
      )
      f1_score = squad_eval.metric_max_over_ground_truths(
          squad_eval.f1_score, best_span_string, answer_strings
      )
      self._total_em += exact_match
      self._total_f1 += f1_score
      self._count += 1
  
file_path: magnitude/pymagnitude/third_party/allennlp/training/metrics/squad_em_and_f1.py
imports: from __future__ import division
  from __future__ import absolute_import
  from allennlp.common import squad_eval
  from allennlp.training.metrics.metric import Metric
label_desc: Use image node_id 2 for calling the SquadEmAndF1 obj's underlying member method code with example usage: obj.__call__(best_span_string, answer_strings) without return types